Chapter 3 Data statistics

load("data/data.Rdata")

3.1 Sequencing reads statistics

sample_metadata %>% 
    summarise(Total=sum(reads_post_fastp * 150 / 1000000000) %>% round(2), 
              mean=mean(reads_post_fastp * 150 / 1000000000) %>% round(2),
              sd=sd(reads_post_fastp * 150 / 1000000000) %>% round(2)) %>%
    unite("Average",mean, sd, sep = " ± ", remove = TRUE) %>%
    tt()

Total	Average
171.92	4.91 ± 1.46

3.2 DNA fractions

sequence_fractions <- read_counts %>%
  pivot_longer(-genome, names_to = "sample", values_to = "value") %>%
  group_by(sample) %>%
  summarise(mags = sum(value)) %>%
    left_join(sample_metadata, by = join_by(sample == sample)) %>%
    select(sample,mags,metagenomic_bases,host_bases,bases_lost_fastp_percent) %>%
    mutate(mags_bases = mags*146) %>%
    mutate(lowqual_bases = ((metagenomic_bases+host_bases)/(1-bases_lost_fastp_percent))-(metagenomic_bases+host_bases)) %>%
    mutate(unmapped_bases = metagenomic_bases - mags_bases) %>%
    mutate(unmapped_bases = ifelse(unmapped_bases < 0, 0, unmapped_bases)) %>%
    select(sample, lowqual_bases, host_bases, unmapped_bases, mags_bases)

sequence_fractions %>%
  mutate_at(vars(-sample), ~./1000000000) %>%
  rename("Sample"=1, "Low quality"=2, "Mapped to host"=3, "Unmapped"=4, "Mapped to MAGs"=5) %>%
  tt()

Sample	Low quality	Mapped to host	Unmapped	Mapped to MAGs
EHI01966	0.3568965	0.005030679	1.9178067	2.507921
EHI01967	0.2692620	0.000709418	0.8339877	4.617569
EHI01968	0.3467658	0.000167184	1.2259195	5.669136
EHI01970	0.2568282	0.000235551	1.1356328	3.939774
EHI01979	0.3068304	0.000429188	1.1643291	3.963429
EHI01982	0.2321305	0.003106822	1.2765585	2.120459
EHI01985	0.3350121	0.000294977	1.3705302	4.091743
EHI01989	0.3249292	0.000570792	1.2444290	3.406020
EHI01992	0.2112149	0.000650860	1.3810604	2.863645
EHI01995	0.2441431	0.001014484	1.0559948	2.625781
EHI02065	0.2881934	0.000248787	0.9495023	3.524029
EHI02067	0.4887927	0.009035522	1.5062498	3.314287
EHI02079	0.4087200	0.001541555	1.0763461	3.216692
EHI02088	0.5380240	0.002508850	1.7069668	4.880649
EHI02097	1.1086051	0.001111047	1.1464450	4.177690
EHI02105	0.4367774	0.006671326	0.7868596	1.606939
EHI02582	0.1565447	0.000041661	0.7099037	3.061117
EHI02584	0.2131393	0.000243149	1.3719978	3.909083
EHI02585	0.3055922	0.000693419	1.3837783	4.156742
EHI02587	0.2002879	0.001053113	1.1718219	3.773711
EHI02592	0.2204754	0.000807086	1.0309674	3.998601
EHI02598	0.1922634	0.000085434	1.1517125	3.984019
EHI02602	0.1798841	0.000148220	1.2512663	2.802077
EHI02603	0.2165665	0.002208379	1.3526444	3.734602
EHI02607	0.2170496	0.000239951	1.5274046	3.978722
EHI02612	0.1376030	0.000170810	0.6921896	2.540514
EHI02615	0.1893992	0.000351599	0.6650246	1.618650
EHI02617	0.1957782	0.000243466	1.3981847	2.879747
EHI02619	0.2292718	0.000086283	0.9636172	3.865591
EHI02624	0.2077297	0.000351284	1.0437963	3.443889
EHI02625	0.1169758	0.000023503	0.5396744	1.996437
EHI02630	0.1994379	0.000258866	0.9279985	3.240507
EHI02632	0.3725667	0.000748839	1.4198031	2.929934
EHI02633	0.2780827	0.000903128	1.3810886	5.477885
EHI02639	0.4685676	0.001126284	2.1791314	8.022893

sequence_fractions %>%
  mutate_at(vars(-sample), ~./1000000000) %>%
  rename("Sample"=1, "Low quality"=2, "Mapped to host"=3, "Unmapped"=4, "Mapped to MAGs"=5) %>%
  summarise(across(where(is.numeric), mean, na.rm = TRUE)) %>%
  tt()

Low quality	Mapped to host	Unmapped	Mapped to MAGs
0.2985812	0.001231758	1.198304	3.598299

sequence_fractions %>%
    pivot_longer(!sample, names_to = "fraction", values_to = "value") %>%
    mutate(value = value / 1000000000) %>%
    mutate(fraction = factor(fraction, levels = c("lowqual_bases","host_bases","unmapped_bases","mags_bases"))) %>%
  
    ggplot(., aes(x = sample, y = value, fill=fraction)) +
        geom_bar(position="stack", stat = "identity") +
      scale_fill_manual(name="Sequence type",
                    breaks=c("lowqual_bases","host_bases","unmapped_bases","mags_bases"),
                    labels=c("Low quality","Mapped to host","Unmapped","Mapped to MAGs"),
                    values=c("#CCCCCC", "#bcdee1", "#d8b8a3","#93655c"))+
        labs(x = "Samples", y = "Amount of data (GB)") +
        theme_classic() +
        theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size=6),legend.position = "bottom")

3.3 Recovered microbial fraction

singlem_table <- sequence_fractions %>%
    mutate(mags_proportion = round((mags_bases / (mags_bases + unmapped_bases))*100,2)) %>%
    left_join(sample_metadata, by = join_by(sample == sample))  %>%
    mutate(singlem_proportion = round(singlem_fraction*100,2)) %>%
    select(sample,mags_proportion,singlem_proportion) %>%
    mutate(mags_proportion = ifelse(singlem_proportion == 0, 0, mags_proportion)) %>% #convert zeros to NA
    mutate(singlem_proportion = ifelse(singlem_proportion == 0, NA, singlem_proportion)) %>% #convert zeros to NA
    mutate(singlem_proportion = ifelse(singlem_proportion < mags_proportion, NA, singlem_proportion)) %>% #if singlem is smaller, then NA, to simplify plot
    mutate(singlem_proportion = ifelse(singlem_proportion > 100, 100, singlem_proportion)) #simplify

singlem_table %>%
    pivot_longer(!sample, names_to = "proportion", values_to = "value") %>%
    mutate(proportion = factor(proportion, levels = c("mags_proportion","singlem_proportion"))) %>%
    ggplot(., aes(x = value, y = sample, color=proportion)) +
            geom_line(aes(group = sample), color = "#f8a538") +
            geom_point() +
      scale_color_manual(name="Proportion",
                    breaks=c("mags_proportion","singlem_proportion"),
                    labels=c("Recovered","Estimated"),
                    values=c("#52e1e8", "#876b53"))+
            theme_classic() +
            labs(y = "Samples", x = "Prokaryotic fraction (%)") +
        theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size=6),legend.position = "right")

damr <- singlem_table %>%
  mutate(damr=ifelse(is.na(singlem_proportion),100,mags_proportion/singlem_proportion*100)) %>%
  select(sample,damr)

damr %>% 
  tt()

sample	damr
EHI01966	100.00000
EHI01967	100.00000
EHI01968	100.00000
EHI01970	100.00000
EHI01979	100.00000
EHI01982	100.00000
EHI01985	100.00000
EHI01989	100.00000
EHI01992	100.00000
EHI01995	100.00000
EHI02065	100.00000
EHI02067	100.00000
EHI02079	100.00000
EHI02088	100.00000
EHI02097	100.00000
EHI02105	100.00000
EHI02582	100.00000
EHI02584	99.78431
EHI02585	100.00000
EHI02587	100.00000
EHI02592	100.00000
EHI02598	100.00000
EHI02602	100.00000
EHI02603	100.00000
EHI02607	100.00000
EHI02612	100.00000
EHI02615	100.00000
EHI02617	100.00000
EHI02619	100.00000
EHI02624	100.00000
EHI02625	100.00000
EHI02630	100.00000
EHI02632	100.00000
EHI02633	100.00000
EHI02639	100.00000

damr %>% 
  filter(damr>95) %>%
  nrow()

[1] 35

damr %>% 
  summarise(mean=mean(damr),sd=sd(damr)) %>% 
  tt()

mean	sd
99.99384	0.03645853